public void New_SimpleTrainAndPredict()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()))
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(new MultiFileSource(dataPath));
// Create prediction engine and test predictions.
var engine = new MyPredictionEngine <SentimentData, SentimentPrediction>(env, model.Transformer);
// Take a couple examples out of the test data and run predictions on top.
var testData = model.Reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath)))
.AsEnumerable <SentimentData>(env, false);
foreach (var input in testData.Take(5))
{
var prediction = engine.Predict(input);
// Verify that predictions match and scores are separated from zero.
Assert.Equal(input.Sentiment, prediction.Sentiment);
Assert.True(input.Sentiment && prediction.Score > 1 || !input.Sentiment && prediction.Score < -1);
}
}
}
public void New_TrainWithInitialPredictor()
{
var dataPath = GetDataPath(SentimentDataPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()));
// Train the pipeline, prepare train set.
var reader = pipeline.Fit(new MultiFileSource(dataPath));
var trainData = reader.Read(new MultiFileSource(dataPath));
// Train the first predictor.
var trainer = new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments
{
NumThreads = 1
}, "Features", "Label");
var firstModel = trainer.Fit(trainData);
// Train the second predictor on the same data.
var secondTrainer = new MyAveragedPerceptron(env, new AveragedPerceptronTrainer.Arguments(), "Features", "Label");
var finalModel = secondTrainer.Train(trainData, firstModel.Model);
}
}
void New_FileBasedSavingOfData()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()));
var trainData = pipeline.Fit(new MultiFileSource(dataPath)).Read(new MultiFileSource(dataPath));
using (var file = env.CreateOutputFile("i.idv"))
trainData.SaveAsBinary(env, file.CreateWriteStream());
var trainer = new MySdca(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label");
var loadedTrainData = new BinaryLoader(env, new BinaryLoader.Arguments(), new MultiFileSource("i.idv"));
// Train.
var model = trainer.Train(loadedTrainData);
DeleteOutputPath("i.idv");
}
}
public void New_Evaluation()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()))
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(new MultiFileSource(dataPath));
// Evaluate on the test set.
var dataEval = model.Read(new MultiFileSource(testDataPath));
var evaluator = new MyBinaryClassifierEvaluator(env, new BinaryClassifierEvaluator.Arguments()
{
});
var metrics = evaluator.Evaluate(dataEval);
}
}
void New_MultithreadedPrediction()
{
var dataPath = GetDataPath(SentimentDataPath);
var testDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()))
.Append(new LinearClassificationTrainer(env, new LinearClassificationTrainer.Arguments {
NumThreads = 1
}, "Features", "Label"));
// Train.
var model = pipeline.Fit(new MultiFileSource(dataPath));
// Create prediction engine and test predictions.
var engine = new MyPredictionEngine <SentimentData, SentimentPrediction>(env, model.Transformer);
// Take a couple examples out of the test data and run predictions on top.
var testData = model.Reader.Read(new MultiFileSource(GetDataPath(SentimentTestPath)))
.AsEnumerable <SentimentData>(env, false);
Parallel.ForEach(testData, (input) =>
{
lock (engine)
{
var prediction = engine.Predict(input);
}
});
}
}
public void New_TrainWithValidationSet()
{
var dataPath = GetDataPath(SentimentDataPath);
var validationDataPath = GetDataPath(SentimentTestPath);
using (var env = new TlcEnvironment(seed: 1, conc: 1))
{
// Pipeline.
var pipeline = new MyTextLoader(env, MakeSentimentTextLoaderArgs())
.Append(new MyTextTransform(env, MakeSentimentTextTransformArgs()));
// Train the pipeline, prepare train and validation set.
var reader = pipeline.Fit(new MultiFileSource(dataPath));
var trainData = reader.Read(new MultiFileSource(dataPath));
var validData = reader.Read(new MultiFileSource(validationDataPath));
// Train model with validation set.
var trainer = new MySdca(env, new Runtime.Learners.LinearClassificationTrainer.Arguments(), "Features", "Label");
var model = trainer.Train(trainData, validData);
}
}
